In this episode, I talk with Amanda Marrs, senior director of product at AMP Robotics about modernizing the world’s recycling infrastructure. Amanda and I talked about how they ensure their models work for a diverse set of objects, measuring the success of their technology, and some tips for building a successful ML team.

“At AMP we have a broad mission of enabling a world without waste.”

“We work backwards on everything that ends up in a landfill to develop the technology we need to keep that from happening.”

“We really have two main areas that we work in. One is technology that we will put in place at a material recovery facility. . . The other half of what we do at AMP is use our own technology for what's called a secondary sortation facility.”

“All of this technology really has three main components. You have to be able to see the objects on the belt, and that's where the machine learning comes in. You have to be able to sort the objects effectively, and there's some ML behind that as well. And then you have to be able to report, see what's happening, and draw conclusions and make decisions and optimize further in the facilities.”

“A majority of the data fits nicely within these primary categories. But, in AI, typically there's this natural long tail, and we have that as well.”

“Diversity is the name of the game in this industry where you have to be able to recognize everything. And so a huge sample set of data really helps us overcome that.”

“The wonderful thing about AI, it doesn't get tired, it doesn't get dizzy. And it can keep its inference at the same rate.”

“What we try to do when we translate this to customers, to non deeply technical folks – they're technical in other ways, but they're not dealing with AI all day – is we really try to translate it to the outcomes.”

“Start your hiring process early so that you're expecting it might take a while before you really, really need that team member joined, onboarded, trained up and enabled to help deliver on projects.”

“I think, for us, recruiting and thinking about what mix of talent we really need on a team, it's looking across all of those different areas and building out a team that really compliments each other's skillsets.”

Resources for Computer Vision Teams:

LinkedIn – Connect with Heather.
Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.
Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.
Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.